Biomedical Engineering ETDs
Publication Date
Summer 7-29-2025
Abstract
Aging is a common risk factor for many chronic diseases, such as cancer, neurodegenerative disease, and heart disease. It has been theorized that a better understanding of aging may yield better preventative treatments for chronic disease. The nematode Caenorhabditis elegans is a widely used model organism for aging research, due to its small size, short lifespan, and wide genetic tool kit. However, manual lifespan scoring, though reliable is time consuming and limits scale. Automation offers a solution to increase the productivity researchers, but existing systems often compromise scalability, accuracy, and throughput, making them unfit for experiments at the genomic scale. To address this, I designed The Worm Automation Machine (WAM), an open source, hands free, image-based automation solution for high throughput C. elegans data collection. WAM combines modular design and machine learning to match the throughput of 8 researchers per hour. The WAM pipeline enables the simultaneous collection of lifespan and healthspan data, supporting larger and more efficient experimental workflows that advance our understanding of the fundamental biology of aging.
Language
English
Keywords
C. elegans, Aging, Lifespan, Healthspan, Automation, Machine learning
Document Type
Thesis
Degree Name
Biomedical Engineering
Level of Degree
Masters
Department Name
Biomedical Engineering
First Committee Member (Chair)
Dr. Mark McCormick
Second Committee Member
Dr. Olga Ponomarova
Third Committee Member
Dr. John King
Recommended Citation
Achusim, Alexander. "Deep Learning and Robotics for High Throughput Lifespan Data Collection in C. elegans." (2025). https://digitalrepository.unm.edu/bme_etds/49
Included in
Biomedical Devices and Instrumentation Commons, Other Medicine and Health Sciences Commons